function [logML,betahat,sigmahat]=logMLVAR_formin(par,y,x,lags,T,n,b,MIN,MAX,SS,Vc,pos,mn,sur,noc,y0,hyperpriors,priorcoef);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This function computes the log-posterior (or the logML if hyperpriors=0), 
% the posterior mode of the coefficients and the covariance matrix of the residuals of the BVAR of 
% Giannone, Lenza and Primiceri (2012)
%
% Last modified: 07/01/2013
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% hyperparameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lambda=MIN.lambda+(MAX.lambda-MIN.lambda)/(1+exp(-par(1)));

d=n+2;

if mn.psi==0;
    psi=SS*(d-n-1);
    if sur==1;
        theta=MIN.theta+(MAX.theta-MIN.theta)/(1+exp(-par(2)));
        if noc==1;
            miu=MIN.miu+(MAX.miu-MIN.miu)/(1+exp(-par(3)));
        end
    elseif sur==0;
        if noc==1;
            miu=MIN.miu+(MAX.miu-MIN.miu)/(1+exp(-par(2)));
        end
    end
elseif mn.psi==1;
    psi=MIN.psi+(MAX.psi-MIN.psi)./(1+exp(-par(2:n+1)));
    if sur==1;
        theta=MIN.theta+(MAX.theta-MIN.theta)/(1+exp(-par(n+2)));
        if noc==1;
            miu=MIN.miu+(MAX.miu-MIN.miu)/(1+exp(-par(n+3)));
        end
    elseif sur==0;
        if noc==1;
            miu=MIN.miu+(MAX.miu-MIN.miu)/(1+exp(-par(n+2)));
        end
    end
end

if mn.alpha==0;
    alpha=2;
elseif mn.alpha==1;
    alpha=MIN.alpha+(MAX.alpha-MIN.alpha)/(1+exp(-par(end)));
end

%% setting up the priors
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
k=1+n*lags;
omega=zeros(k,1);
omega(1)=Vc;
for i=1:lags
    omega(1+(i-1)*n+1:1+i*n)=(d-n-1)*(lambda^2)*(1/(i^alpha))./psi;
end

% prior scale matrix for the covariance of the shocks
PSI=diag(psi);

Td=0;
xdsur=[];
ydsur=[];
xdnoc=[];
ydnoc=[];
% dummy observations if sur and/or noc = 1
if sur==1;
    
    xdsur=[1/theta (1/theta)*repmat(y0,1,lags)];
    ydsur=(1/theta)*y0;
    
    y=[y;ydsur];
    x=[x;xdsur];
    Td=1;
end

if noc==1;
        
    ydnoc=(1/miu)*diag(y0); ydnoc(pos,pos)=0;
    xdnoc=[zeros(n,1) (1/miu)*repmat(diag(y0),1,lags)];
    
    y=[y;ydnoc];
    x=[x;xdnoc];
    Td=Td+n;
end
T=T+Td;

%% output
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% posterior mode of the VAR coefficients
betahat=(x'*x+diag(1./omega))\(x'*y+diag(1./omega)*b);

% VAR residuals
epshat=y-x*betahat;

% Posterior mode of the covariance matrix
sigmahat=(epshat'*epshat + PSI + (betahat-b)'*diag(1./omega)*(betahat-b))/(T+d+n+1);

% logML
aaa=diag(sqrt(omega))*(x'*x)*diag(sqrt(omega));
bbb=diag(1./sqrt(psi))*(epshat'*epshat + (betahat-b)'*diag(1./omega)*(betahat-b))*diag(1./sqrt(psi));

eigaaa=real(eig(aaa)); eigaaa(eigaaa<1e-12)=0; eigaaa=eigaaa+1;
eigbbb=real(eig(bbb)); eigbbb(eigbbb<1e-12)=0; eigbbb=eigbbb+1;

logML = - n*T*log(pi)/2 + sum(gammaln((T+d-[0:n-1])/2)-gammaln((d-[0:n-1])/2)) +...
    - T*sum(log(psi))/2 - n*sum(log(eigaaa))/2 - (T+d)*sum(log(eigbbb))/2;


if sur==1 | noc==1;
    yd=[ydsur;ydnoc];
    xd=[xdsur;xdnoc];
    
    % prior mode of the VAR coefficients
    % betahatd=(xd'*xd+diag(1./omega))\(xd'*yd+diag(1./omega)*b);
    betahatd=b;     % this is the case for our priors (the line above delivers the same but is numerically not very stable)
    
    % VAR residuals at the prior mode
    epshatd=yd-xd*betahatd;
    
    aaa=diag(sqrt(omega))*(xd'*xd)*diag(sqrt(omega));
    bbb=diag(1./sqrt(psi))*(epshatd'*epshatd + (betahatd-b)'*diag(1./omega)*(betahatd-b))*diag(1./sqrt(psi));
    
    eigaaa=real(eig(aaa)); eigaaa(eigaaa<1e-12)=0; eigaaa=eigaaa+1;
    eigbbb=real(eig(bbb)); eigbbb(eigbbb<1e-12)=0; eigbbb=eigbbb+1;
    
    % normalizing constant
    norm = - n*Td*log(pi)/2 + sum(gammaln((Td+d-[0:n-1])/2)-gammaln((d-[0:n-1])/2)) +...
           - Td*sum(log(psi))/2 - n*sum(log(eigaaa))/2 - (T+d)*sum(log(eigbbb))/2;
    
    logML=logML-norm;
end


if hyperpriors==1;
    logML=logML+logGammapdf(lambda,priorcoef.lambda.k,priorcoef.lambda.theta);
    if sur==1;
        logML=logML+logGammapdf(theta,priorcoef.theta.k,priorcoef.theta.theta);
    end
    if noc==1;
        logML=logML+logGammapdf(miu,priorcoef.miu.k,priorcoef.miu.theta);
    end
    if mn.psi==1;
        logML=logML+sum(logIG2pdf(psi/(d-n-1),priorcoef.alpha.PSI,priorcoef.beta.PSI));
    end
end
logML=-logML;


function r=logGammapdf(x,k,theta);
r=(k-1)*log(x)-x/theta-k*log(theta)-gammaln(k);

function r=logIG2pdf(x,alpha,beta);
r=alpha*log(beta)-(alpha+1)*log(x)-beta./x-gammaln(alpha);